Sustained virological response after treatment with direct antiviral agents in individuals with HIV and hepatitis C co‐infection

Abstract Introduction Randomized trials and observational studies have consistently reported rates of sustained virological response (SVR), equivalent to hepatitis C virus (HCV) cure, as high as 95% following treatment with direct‐acting antiviral (DAA) treatment in individuals with HIV and HCV co‐infection. However, large studies assessing whether SVR rates differ according to demographic and clinical strata are lacking. Additionally, the SVR rates reported in the literature were typically computed in non‐random samples of individuals with available post‐DAA HCV‐RNA measures. Here, we aimed to estimate the probability of SVR after DAA treatment initiation in persons with HIV and HCV co‐infection overall and by demographic and clinical characteristics with and without adjustment for missing HCV‐RNA testing. Methods We included adults with HIV‐HCV co‐infection who received DAA treatment between 2014 and 2020 in HepCAUSAL, an international collaboration of cohorts from Europe and North America. We estimated the proportions of DAA recipients who had documented SVR (defined as an undetectable HCV‐RNA at least 12 weeks after the end of DAA treatment) overall and by strata defined by age, sex, presence of cirrhosis, calendar period, mode of HIV acquisition, CD4 cell count and HCV genotype at DAA treatment. We then compared these rates with those obtained using the parametric g‐formula to impute SVR status for individuals with no SVR assessment. Results and Discussion A total of 4527 individuals who initiated DAA treatment (88% males, median [IQR] age 56 [50, 62] years) were included. Of the total of 642 (14%) individuals had no HCV‐RNA test on or after 12 weeks after the end of treatment. The overall observed and g‐formula imputed SVR rates were 93% (95% CI 93, 94) and 94% (95% CI 92, 95), respectively. SVR estimates were similarly high across all strata. A substantial proportion of individuals who received DAA treatment were never assessed for SVR post‐DAA and strategies for more systematic routine HCV‐RNA testing should be considered. Conclusions Our estimates with and without adjustment for missing HCV‐RNA testing indicate SVR rates of approximately 95%, like those reported in clinical trials.

Boston Medical Center Cohort PI: Sara Lodi, Boston University School of Public Health. The goal of this newly established cohort is to examine clinical and public health questions about HIV disease, hepatitis C disease and substance abuse, and their intersection in era of the opioid epidemic. The Boston Medical Center is the largest safety net hospital in New England and it functions as the primary site of care for a diverse urban population comprising groups with typical high prevalence of HCV infection, HIV infection, and substance abuse such as the homeless and low-income patients. The database consists of electronic medical records collected in routine clinical practice at the Boston Medical Center. The cohort includes adult individuals with HIV infection, chronic hepatitis C, and/or a diagnostic code for opioid use disorder between 1/1/2005 and 1/1/2018. Data collection includes all diagnostic codes, prescriptions, procedures, laboratory test dates, and results; self-reported smoking habits, alcohol use, and substance use; risk group for HIV-positive individuals, death, and emergency room visits. The database was pooled in June 2018. The cohort includes approximately 8,000 HIV-The Icona Foundation Study cohort is an observational multicentre cohort that enrolls HIV-infected individuals who are antiretroviral-naïve at the time of enrollment. Patients are voluntary enrolled by physicians at the different centers in Italy participating in ICONA Study after signing an informed consent. This cohort was set up in January 1997 and currently includes data on patients enrolled at 51 infectious disease units in Italy. The ICONA database includes all events over the follow-up (i.e., all laboratory measurements, clinical events, medication and treatment changes, hospitalizations, and death); in their absence, a follow-up visit is scheduled at least every 6 months.
Southern Alberta Clinic Cohort PI: Dr. John Gill, Southern Alberta HIV Clinic, Canada. The Southern Alberta Clinic Cohort is a geographically defined clinical cohort of all HIV infected patients receiving their HIV care in S. Alberta. The clinic is the primary site of care for 93% of HIV positive patients with 3 secondary sites serving specific populations (7%). The prospective cohort started in 1989 has ongoing rolling recruitment (retroactive data entry [1984][1985][1986][1987][1988][1989]. Total number in cohort dataset was >5050 on Dec 31 2021: Female 23%, Risk groups: MSM (48%); IDUs (17%); Heterosexuals including migrants (31%); perinatal Transmission: 0.27% other or unknown (2%). Current active roster 2025. Our database/EMR collects and retains very extensive demographic, therapeutic, laboratory (HIV and non HIV), clinical , hospitalization, social, public health, and cost data on every encounter of our patients within the Province. The clinic has over 400 peer reviewed publications. Current research includes mortality and causes of death, toxicity and adverse event studies, economic and outcome analysis, HIV Epidemiology, resistance in therapy naive patients, acute seroconversion illness , HIV phylogenetics , adherence, intimate partner violence and HIV, churn effects, HIV and housing, and treatment outcomes.

Parametric g-formula
The g-formula is a generalization of standardization to the time-varying setting. When the measured variables are sufficient to adjust for confounding and selection bias [1,2], the gformula under a specified intervention identifies the outcome distribution in the study population had that intervention been, possibly contrary to fact, implemented in all individuals in the population. The parametric g-formula is an approach to estimate the components of the g-formula using parametric models [3][4][5]. For this study, the outcome was sustained virological response (SVR) and the intervention was receiving an HCV-RNA test between 10 and 24 months after end of treatment (SVR assessment window) under a prespecified frequency of HCV-RNA testing in the SVR assessment window. The parametric g-formula algorithm has two steps. In the first step, we fit parametric regression models to estimate the distribution of outcome and time-varying covariates in the SVR assessment window conditional on prior history. Second, we standardize the probability of the outcome to the conditional distributions, both estimated in step 1, under the intervention values.
For the first step, we fit separate logistic regression models for time-varying indicators at time t (HCV-RNA test, ALT test, AST test, and platelet count test) and linear regression models for continuous variables on the natural logarithm scale (AST, ALT, and platelet count). All these models included as covariates the most recent value of the time-varying covariates (time since end DAA treatment, time since last HCV-RNA test, AST, ALT, platelet count) and baseline covariates age (<35,35-50,>50 years), sex, mode of HIV acquisition, cohort, calendar period, CD4 cell count category (<350, 350-500,>500 cells/mm3), HIV virological suppression (HIV-RNA ≤50, >50 copies/mL), history of antiretroviral treatment for HIV, history of AIDS, hepatitis B virus co-infection (presence of either hepatitis B surface antigen or detectable hepatitis B virus DNA), prior HCV treatment with interferon, HCV genotype, and fibrosis stage categorized as no significant fibrosis (FIB-4<1.45), significant fibrosis (FIB-4≥1.45 and FIB-4≤3.25), and cirrhosis (FIB-4>3.25).
For the second step, because the standardization involves a complex sum, we approximate it using Monte Carlo simulation. We then estimated the population SVR proportion as the average of the subject-specific probability of achieving SVR by the end of the SVR assessment window.
Finally, we used a nonparametric bootstrap procedure based on 500 samples to obtain percentile-based 95% confidence intervals.